Overview

Dataset statistics

Number of variables14
Number of observations5570
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory189.9 B

Variable types

Numeric13
Categorical1

Alerts

Município has a high cardinality: 5570 distinct values High cardinality
CV_HEPatite_B is highly correlated with CV_BCG and 10 other fieldsHigh correlation
CV_HIB is highly correlated with CV_BCG and 10 other fieldsHigh correlation
CV_DPT is highly correlated with CV_BCG and 10 other fieldsHigh correlation
CV_Polio is highly correlated with CV_BCG and 10 other fieldsHigh correlation
CV_rota is highly correlated with CV_HEPatite_B and 9 other fieldsHigh correlation
CV_Pneumo is highly correlated with CV_BCG and 10 other fieldsHigh correlation
CV_MNCC is highly correlated with CV_HEPatite_B and 9 other fieldsHigh correlation
CV_SCR1 is highly correlated with CV_BCG and 10 other fieldsHigh correlation
CV_SCR2 is highly correlated with CV_BCG and 10 other fieldsHigh correlation
CV_VARICELA is highly correlated with CV_BCG and 10 other fieldsHigh correlation
CV_HEPATITE_A is highly correlated with CV_BCG and 10 other fieldsHigh correlation
CV_BCG is highly correlated with CV_HEPatite_B and 8 other fieldsHigh correlation
Município is uniformly distributed Uniform
COD has unique values Unique
Município has unique values Unique
CV_BCG has 431 (7.7%) zeros Zeros
CV_SCR2 has 136 (2.4%) zeros Zeros

Reproduction

Analysis started2022-11-09 00:44:44.842151
Analysis finished2022-11-09 00:45:07.378372
Duration22.54 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

COD
Real number (ℝ≥0)

UNIQUE

Distinct5570
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean325358.6278
Minimum110001
Maximum530010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:45:07.486377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum110001
5-th percentile150777.25
Q1251212.5
median314627.5
Q3411918.75
95-th percentile510729.55
Maximum530010
Range420009
Interquartile range (IQR)160706.25

Descriptive statistics

Standard deviation98491.03388
Coefficient of variation (CV)0.3027152977
Kurtosis-0.5258091553
Mean325358.6278
Median Absolute Deviation (MAD)74152.5
Skewness0.1213411839
Sum1812247557
Variance9700483754
MonotonicityNot monotonic
2022-11-08T21:45:07.617372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1100011
 
< 0.1%
3539701
 
< 0.1%
3540401
 
< 0.1%
3540301
 
< 0.1%
3540251
 
< 0.1%
3540201
 
< 0.1%
3540101
 
< 0.1%
3540001
 
< 0.1%
3539901
 
< 0.1%
3539801
 
< 0.1%
Other values (5560)5560
99.8%
ValueCountFrequency (%)
1100011
< 0.1%
1100021
< 0.1%
1100031
< 0.1%
1100041
< 0.1%
1100051
< 0.1%
1100061
< 0.1%
1100071
< 0.1%
1100081
< 0.1%
1100091
< 0.1%
1100101
< 0.1%
ValueCountFrequency (%)
5300101
< 0.1%
5222301
< 0.1%
5222201
< 0.1%
5222051
< 0.1%
5222001
< 0.1%
5221901
< 0.1%
5221851
< 0.1%
5221801
< 0.1%
5221701
< 0.1%
5221601
< 0.1%

Município
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct5570
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size467.3 KiB
110001 Alta Floresta D'Oeste
 
1
353970 Platina
 
1
354040 Populina
 
1
354030 Pontes Gestal
 
1
354025 Pontalinda
 
1
Other values (5565)
5565 

Length

Max length39
Median length34
Mean length18.61059246
Min length10

Characters and Unicode

Total characters103661
Distinct characters80
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5570 ?
Unique (%)100.0%

Sample

1st row110001 Alta Floresta D'Oeste
2nd row110002 Ariquemes
3rd row110003 Cabixi
4th row110004 Cacoal
5th row110005 Cerejeiras

Common Values

ValueCountFrequency (%)
110001 Alta Floresta D'Oeste1
 
< 0.1%
353970 Platina1
 
< 0.1%
354040 Populina1
 
< 0.1%
354030 Pontes Gestal1
 
< 0.1%
354025 Pontalinda1
 
< 0.1%
354020 Pontal1
 
< 0.1%
354010 Pongaí1
 
< 0.1%
354000 Pompéia1
 
< 0.1%
353990 Poloni1
 
< 0.1%
353980 Poá1
 
< 0.1%
Other values (5560)5560
99.8%

Length

2022-11-08T21:45:07.748376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
do756
 
4.8%
são364
 
2.3%
de302
 
1.9%
santa161
 
1.0%
da143
 
0.9%
nova135
 
0.9%
sul115
 
0.7%
rio94
 
0.6%
dos73
 
0.5%
josé70
 
0.4%
Other values (9533)13640
86.0%

Most occurring characters

ValueCountFrequency (%)
10283
 
9.9%
a8791
 
8.5%
08160
 
7.9%
o5961
 
5.8%
14774
 
4.6%
24591
 
4.4%
r4532
 
4.4%
i4388
 
4.2%
34106
 
4.0%
e3764
 
3.6%
Other values (70)44311
42.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter50872
49.1%
Decimal Number33420
32.2%
Space Separator10283
 
9.9%
Uppercase Letter9010
 
8.7%
Other Punctuation47
 
< 0.1%
Dash Punctuation29
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a8791
17.3%
o5961
11.7%
r4532
8.9%
i4388
8.6%
e3764
 
7.4%
n3196
 
6.3%
d2553
 
5.0%
s2423
 
4.8%
t2293
 
4.5%
u2155
 
4.2%
Other values (27)10816
21.3%
Uppercase Letter
ValueCountFrequency (%)
S1137
12.6%
C970
10.8%
P911
 
10.1%
M721
 
8.0%
A698
 
7.7%
B602
 
6.7%
I475
 
5.3%
J405
 
4.5%
G391
 
4.3%
R367
 
4.1%
Other values (20)2333
25.9%
Decimal Number
ValueCountFrequency (%)
08160
24.4%
14774
14.3%
24591
13.7%
34106
12.3%
53654
10.9%
42781
 
8.3%
71470
 
4.4%
61422
 
4.3%
91382
 
4.1%
81080
 
3.2%
Space Separator
ValueCountFrequency (%)
10283
100.0%
Other Punctuation
ValueCountFrequency (%)
'47
100.0%
Dash Punctuation
ValueCountFrequency (%)
-29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin59882
57.8%
Common43779
42.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a8791
14.7%
o5961
 
10.0%
r4532
 
7.6%
i4388
 
7.3%
e3764
 
6.3%
n3196
 
5.3%
d2553
 
4.3%
s2423
 
4.0%
t2293
 
3.8%
u2155
 
3.6%
Other values (57)19826
33.1%
Common
ValueCountFrequency (%)
10283
23.5%
08160
18.6%
14774
10.9%
24591
10.5%
34106
 
9.4%
53654
 
8.3%
42781
 
6.4%
71470
 
3.4%
61422
 
3.2%
91382
 
3.2%
Other values (3)1156
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII100822
97.3%
None2839
 
2.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10283
 
10.2%
a8791
 
8.7%
08160
 
8.1%
o5961
 
5.9%
14774
 
4.7%
24591
 
4.6%
r4532
 
4.5%
i4388
 
4.4%
34106
 
4.1%
e3764
 
3.7%
Other values (54)41472
41.1%
None
ValueCountFrequency (%)
ã794
28.0%
á393
13.8%
í336
11.8%
é317
 
11.2%
ç268
 
9.4%
ó243
 
8.6%
â161
 
5.7%
ú101
 
3.6%
ô71
 
2.5%
ê70
 
2.5%
Other values (6)85
 
3.0%

CV_BCG
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct3579
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.90465171
Minimum0
Maximum594.72
Zeros431
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:45:07.877372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115.56
median48.72
Q380.6025
95-th percentile116.226
Maximum594.72
Range594.72
Interquartile range (IQR)65.0425

Descriptive statistics

Standard deviation42.85430973
Coefficient of variation (CV)0.825635243
Kurtosis12.01724139
Mean51.90465171
Median Absolute Deviation (MAD)32.525
Skewness1.781430416
Sum289108.91
Variance1836.491862
MonotonicityNot monotonic
2022-11-08T21:45:08.006373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0431
 
7.7%
10032
 
0.6%
5023
 
0.4%
2014
 
0.3%
7513
 
0.2%
16.6713
 
0.2%
66.6712
 
0.2%
6011
 
0.2%
6.6711
 
0.2%
18.7511
 
0.2%
Other values (3569)4999
89.7%
ValueCountFrequency (%)
0431
7.7%
0.111
 
< 0.1%
0.131
 
< 0.1%
0.211
 
< 0.1%
0.271
 
< 0.1%
0.311
 
< 0.1%
0.331
 
< 0.1%
0.392
 
< 0.1%
0.411
 
< 0.1%
0.471
 
< 0.1%
ValueCountFrequency (%)
594.721
< 0.1%
500.191
< 0.1%
473.921
< 0.1%
437.571
< 0.1%
396.371
< 0.1%
379.971
< 0.1%
340.541
< 0.1%
3101
< 0.1%
291.81
< 0.1%
285.711
< 0.1%

CV_HEPatite_B
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3329
Distinct (%)59.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.40500718
Minimum0
Maximum430.77
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:45:08.137374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.3
Q170.43
median84.33
Q397.04
95-th percentile120
Maximum430.77
Range430.77
Interquartile range (IQR)26.61

Descriptive statistics

Standard deviation25.00849407
Coefficient of variation (CV)0.2998440371
Kurtosis10.12256374
Mean83.40500718
Median Absolute Deviation (MAD)13.23
Skewness0.7267847412
Sum464565.89
Variance625.4247757
MonotonicityNot monotonic
2022-11-08T21:45:08.387374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10090
 
1.6%
83.3326
 
0.5%
66.6720
 
0.4%
7518
 
0.3%
8018
 
0.3%
85.7118
 
0.3%
9018
 
0.3%
88.8917
 
0.3%
71.4316
 
0.3%
77.7815
 
0.3%
Other values (3319)5314
95.4%
ValueCountFrequency (%)
05
0.1%
0.261
 
< 0.1%
3.481
 
< 0.1%
3.511
 
< 0.1%
3.751
 
< 0.1%
4.761
 
< 0.1%
5.361
 
< 0.1%
6.121
 
< 0.1%
6.211
 
< 0.1%
6.672
 
< 0.1%
ValueCountFrequency (%)
430.771
< 0.1%
3301
< 0.1%
2501
< 0.1%
231.581
< 0.1%
222.571
< 0.1%
221.431
< 0.1%
2161
< 0.1%
2001
< 0.1%
197.731
< 0.1%
190.911
< 0.1%

CV_HIB
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3322
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.42763914
Minimum0
Maximum423.08
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:45:08.513371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.3045
Q170.4725
median84.38
Q397.1075
95-th percentile120
Maximum423.08
Range423.08
Interquartile range (IQR)26.635

Descriptive statistics

Standard deviation25.00687727
Coefficient of variation (CV)0.2997433169
Kurtosis9.551395468
Mean83.42763914
Median Absolute Deviation (MAD)13.265
Skewness0.6938169252
Sum464691.95
Variance625.3439109
MonotonicityNot monotonic
2022-11-08T21:45:08.638373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10093
 
1.7%
83.3324
 
0.4%
88.8918
 
0.3%
9018
 
0.3%
8018
 
0.3%
7517
 
0.3%
66.6717
 
0.3%
85.7116
 
0.3%
71.4315
 
0.3%
87.514
 
0.3%
Other values (3312)5320
95.5%
ValueCountFrequency (%)
05
0.1%
0.261
 
< 0.1%
3.481
 
< 0.1%
3.511
 
< 0.1%
3.751
 
< 0.1%
4.761
 
< 0.1%
5.361
 
< 0.1%
5.921
 
< 0.1%
6.121
 
< 0.1%
6.672
 
< 0.1%
ValueCountFrequency (%)
423.081
< 0.1%
3301
< 0.1%
2501
< 0.1%
231.581
< 0.1%
221.631
< 0.1%
221.431
< 0.1%
2161
< 0.1%
2001
< 0.1%
197.731
< 0.1%
190.911
< 0.1%

CV_DPT
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3305
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.54537882
Minimum0
Maximum423.08
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:45:08.763376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.419
Q170.59
median84.48
Q397.2275
95-th percentile120
Maximum423.08
Range423.08
Interquartile range (IQR)26.6375

Descriptive statistics

Standard deviation24.9990235
Coefficient of variation (CV)0.2992268855
Kurtosis9.563700409
Mean83.54537882
Median Absolute Deviation (MAD)13.19
Skewness0.6967920267
Sum465347.76
Variance624.951176
MonotonicityNot monotonic
2022-11-08T21:45:08.891372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10093
 
1.7%
83.3327
 
0.5%
8022
 
0.4%
9018
 
0.3%
88.8918
 
0.3%
7517
 
0.3%
71.4315
 
0.3%
85.7114
 
0.3%
66.6714
 
0.3%
87.513
 
0.2%
Other values (3295)5319
95.5%
ValueCountFrequency (%)
05
0.1%
0.261
 
< 0.1%
3.481
 
< 0.1%
3.511
 
< 0.1%
3.751
 
< 0.1%
4.761
 
< 0.1%
5.361
 
< 0.1%
5.921
 
< 0.1%
6.121
 
< 0.1%
6.672
 
< 0.1%
ValueCountFrequency (%)
423.081
< 0.1%
3301
< 0.1%
2501
< 0.1%
231.581
< 0.1%
221.631
< 0.1%
221.431
< 0.1%
2161
< 0.1%
2001
< 0.1%
197.731
< 0.1%
190.911
< 0.1%

CV_Polio
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3312
Distinct (%)59.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.48775224
Minimum0
Maximum384.62
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:45:09.018376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.366
Q169.27
median83.465
Q395.9975
95-th percentile118.4525
Maximum384.62
Range384.62
Interquartile range (IQR)26.7275

Descriptive statistics

Standard deviation24.68308295
Coefficient of variation (CV)0.2992333077
Kurtosis7.808414651
Mean82.48775224
Median Absolute Deviation (MAD)13.18
Skewness0.5924374076
Sum459456.78
Variance609.2545837
MonotonicityNot monotonic
2022-11-08T21:45:09.149371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10089
 
1.6%
8022
 
0.4%
7521
 
0.4%
66.6719
 
0.3%
83.3318
 
0.3%
85.7118
 
0.3%
88.8918
 
0.3%
9016
 
0.3%
5014
 
0.3%
91.6714
 
0.3%
Other values (3302)5321
95.5%
ValueCountFrequency (%)
05
0.1%
0.261
 
< 0.1%
1.621
 
< 0.1%
1.751
 
< 0.1%
3.481
 
< 0.1%
3.751
 
< 0.1%
4.081
 
< 0.1%
5.921
 
< 0.1%
5.951
 
< 0.1%
6.673
0.1%
ValueCountFrequency (%)
384.621
< 0.1%
3301
< 0.1%
2501
< 0.1%
234.211
< 0.1%
228.571
< 0.1%
2241
< 0.1%
219.441
< 0.1%
2001
< 0.1%
197.731
< 0.1%
190.911
< 0.1%

CV_rota
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3355
Distinct (%)60.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.20124417
Minimum0
Maximum370
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:45:09.278371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39.573
Q168.54
median83.235
Q396
95-th percentile120
Maximum370
Range370
Interquartile range (IQR)27.46

Descriptive statistics

Standard deviation25.17800552
Coefficient of variation (CV)0.3062971342
Kurtosis7.573239546
Mean82.20124417
Median Absolute Deviation (MAD)13.735
Skewness0.5994734931
Sum457860.93
Variance633.9319618
MonotonicityNot monotonic
2022-11-08T21:45:09.407372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10084
 
1.5%
7524
 
0.4%
66.6717
 
0.3%
5017
 
0.3%
87.516
 
0.3%
83.3316
 
0.3%
8016
 
0.3%
12015
 
0.3%
71.4315
 
0.3%
88.8914
 
0.3%
Other values (3345)5336
95.8%
ValueCountFrequency (%)
08
0.1%
0.721
 
< 0.1%
0.871
 
< 0.1%
1.751
 
< 0.1%
3.851
 
< 0.1%
41
 
< 0.1%
5.261
 
< 0.1%
5.561
 
< 0.1%
5.771
 
< 0.1%
5.951
 
< 0.1%
ValueCountFrequency (%)
3701
< 0.1%
353.851
< 0.1%
271.431
< 0.1%
2681
< 0.1%
228.571
< 0.1%
207.691
< 0.1%
2002
< 0.1%
192.541
< 0.1%
185.711
< 0.1%
181.821
< 0.1%

CV_Pneumo
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3363
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.16659066
Minimum0
Maximum440
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:45:09.538371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43.149
Q171.865
median86.11
Q398.78
95-th percentile123.08
Maximum440
Range440
Interquartile range (IQR)26.915

Descriptive statistics

Standard deviation25.3644533
Coefficient of variation (CV)0.2978216353
Kurtosis11.55940878
Mean85.16659066
Median Absolute Deviation (MAD)13.45
Skewness0.8200680335
Sum474377.91
Variance643.3554913
MonotonicityNot monotonic
2022-11-08T21:45:09.677372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10082
 
1.5%
83.3323
 
0.4%
7523
 
0.4%
66.6721
 
0.4%
8015
 
0.3%
9015
 
0.3%
90.4815
 
0.3%
88.8914
 
0.3%
12013
 
0.2%
90.9112
 
0.2%
Other values (3353)5337
95.8%
ValueCountFrequency (%)
07
0.1%
1.081
 
< 0.1%
1.741
 
< 0.1%
2.671
 
< 0.1%
3.511
 
< 0.1%
3.751
 
< 0.1%
5.951
 
< 0.1%
6.672
 
< 0.1%
6.941
 
< 0.1%
7.141
 
< 0.1%
ValueCountFrequency (%)
4401
< 0.1%
369.231
< 0.1%
2601
< 0.1%
257.141
< 0.1%
228.571
< 0.1%
208.331
< 0.1%
207.691
< 0.1%
205.971
< 0.1%
2002
< 0.1%
190.911
< 0.1%

CV_MNCC
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3343
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.74824776
Minimum0
Maximum400
Zeros9
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:45:09.804374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.427
Q170
median83.61
Q396.24
95-th percentile118.9915
Maximum400
Range400
Interquartile range (IQR)26.24

Descriptive statistics

Standard deviation24.67232558
Coefficient of variation (CV)0.2981613056
Kurtosis10.24818455
Mean82.74824776
Median Absolute Deviation (MAD)13.085
Skewness0.6982630431
Sum460907.74
Variance608.7236498
MonotonicityNot monotonic
2022-11-08T21:45:09.937371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10097
 
1.7%
8025
 
0.4%
7520
 
0.4%
88.8920
 
0.4%
66.6720
 
0.4%
83.3319
 
0.3%
5014
 
0.3%
81.2514
 
0.3%
71.4314
 
0.3%
81.8213
 
0.2%
Other values (3333)5314
95.4%
ValueCountFrequency (%)
09
0.2%
2.671
 
< 0.1%
2.861
 
< 0.1%
3.511
 
< 0.1%
3.751
 
< 0.1%
51
 
< 0.1%
5.411
 
< 0.1%
6.671
 
< 0.1%
7.142
 
< 0.1%
7.411
 
< 0.1%
ValueCountFrequency (%)
4001
< 0.1%
376.921
< 0.1%
2721
< 0.1%
218.181
< 0.1%
214.291
< 0.1%
207.691
< 0.1%
2001
< 0.1%
181.251
< 0.1%
179.11
< 0.1%
177.141
< 0.1%

CV_SCR1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3402
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.06631239
Minimum0
Maximum430
Zeros8
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:45:10.070372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41.8545
Q173.2175
median88.995
Q3102.5
95-th percentile128.891
Maximum430
Range430
Interquartile range (IQR)29.2825

Descriptive statistics

Standard deviation27.73196394
Coefficient of variation (CV)0.314898662
Kurtosis8.860177843
Mean88.06631239
Median Absolute Deviation (MAD)14.455
Skewness0.876399964
Sum490529.36
Variance769.0618239
MonotonicityNot monotonic
2022-11-08T21:45:10.197372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100119
 
2.1%
7521
 
0.4%
85.7118
 
0.3%
8017
 
0.3%
83.3316
 
0.3%
71.4314
 
0.3%
116.6714
 
0.3%
133.3312
 
0.2%
12511
 
0.2%
11011
 
0.2%
Other values (3392)5317
95.5%
ValueCountFrequency (%)
08
0.1%
2.51
 
< 0.1%
3.111
 
< 0.1%
3.571
 
< 0.1%
42
 
< 0.1%
4.051
 
< 0.1%
4.481
 
< 0.1%
6.021
 
< 0.1%
6.141
 
< 0.1%
6.251
 
< 0.1%
ValueCountFrequency (%)
4301
< 0.1%
369.231
< 0.1%
288.461
< 0.1%
285.711
< 0.1%
252.171
< 0.1%
2521
< 0.1%
2501
< 0.1%
232.761
< 0.1%
229.631
< 0.1%
228.571
< 0.1%

CV_SCR2
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct3642
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.94277199
Minimum0
Maximum410
Zeros136
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:45:10.440372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.67
Q137.3525
median60.98
Q382.25
95-th percentile109.09
Maximum410
Range410
Interquartile range (IQR)44.8975

Descriptive statistics

Standard deviation31.76216873
Coefficient of variation (CV)0.5298748735
Kurtosis2.88765018
Mean59.94277199
Median Absolute Deviation (MAD)22.35
Skewness0.4097075555
Sum333881.24
Variance1008.835362
MonotonicityNot monotonic
2022-11-08T21:45:10.566372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0136
 
2.4%
10059
 
1.1%
66.6724
 
0.4%
5021
 
0.4%
83.3318
 
0.3%
7518
 
0.3%
33.3315
 
0.3%
84.6214
 
0.3%
2514
 
0.3%
37.513
 
0.2%
Other values (3632)5238
94.0%
ValueCountFrequency (%)
0136
2.4%
0.211
 
< 0.1%
0.261
 
< 0.1%
0.291
 
< 0.1%
0.431
 
< 0.1%
0.531
 
< 0.1%
0.581
 
< 0.1%
0.611
 
< 0.1%
0.71
 
< 0.1%
0.711
 
< 0.1%
ValueCountFrequency (%)
4101
< 0.1%
292.311
< 0.1%
233.331
< 0.1%
2281
< 0.1%
2001
< 0.1%
182.611
< 0.1%
1801
< 0.1%
161.541
< 0.1%
158.821
< 0.1%
157.141
< 0.1%

CV_VARICELA
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3545
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.09573429
Minimum0
Maximum370
Zeros14
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:45:10.692372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.707
Q161.915
median81.25
Q396.6775
95-th percentile125
Maximum370
Range370
Interquartile range (IQR)34.7625

Descriptive statistics

Standard deviation30.17910747
Coefficient of variation (CV)0.3767879493
Kurtosis3.885998434
Mean80.09573429
Median Absolute Deviation (MAD)17
Skewness0.6246051422
Sum446133.24
Variance910.7785276
MonotonicityNot monotonic
2022-11-08T21:45:10.825371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10066
 
1.2%
7521
 
0.4%
8020
 
0.4%
66.6717
 
0.3%
87.516
 
0.3%
83.3315
 
0.3%
12015
 
0.3%
014
 
0.3%
88.8914
 
0.3%
5013
 
0.2%
Other values (3535)5359
96.2%
ValueCountFrequency (%)
014
0.3%
0.31
 
< 0.1%
0.911
 
< 0.1%
1.31
 
< 0.1%
2.041
 
< 0.1%
2.421
 
< 0.1%
2.781
 
< 0.1%
2.861
 
< 0.1%
3.511
 
< 0.1%
3.881
 
< 0.1%
ValueCountFrequency (%)
3701
< 0.1%
284.621
< 0.1%
264.291
< 0.1%
254.551
< 0.1%
242.421
< 0.1%
233.331
< 0.1%
231.581
< 0.1%
228.261
< 0.1%
226.471
< 0.1%
222.891
< 0.1%

CV_HEPATITE_A
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3370
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.59285637
Minimum0
Maximum470
Zeros24
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size43.6 KiB
2022-11-08T21:45:10.952373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile31.3725
Q164.715
median81.48
Q395.6875
95-th percentile119.05
Maximum470
Range470
Interquartile range (IQR)30.9725

Descriptive statistics

Standard deviation27.24634795
Coefficient of variation (CV)0.3423215247
Kurtosis11.15375329
Mean79.59285637
Median Absolute Deviation (MAD)15.28
Skewness0.6491802866
Sum443332.21
Variance742.3634766
MonotonicityNot monotonic
2022-11-08T21:45:11.080373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10097
 
1.7%
7529
 
0.5%
5024
 
0.4%
024
 
0.4%
66.6721
 
0.4%
85.7119
 
0.3%
8019
 
0.3%
83.3316
 
0.3%
81.8213
 
0.2%
111.1113
 
0.2%
Other values (3360)5295
95.1%
ValueCountFrequency (%)
024
0.4%
0.251
 
< 0.1%
0.361
 
< 0.1%
1.111
 
< 0.1%
1.241
 
< 0.1%
1.611
 
< 0.1%
1.81
 
< 0.1%
1.851
 
< 0.1%
1.921
 
< 0.1%
2.381
 
< 0.1%
ValueCountFrequency (%)
4701
< 0.1%
376.921
< 0.1%
271.431
< 0.1%
2401
< 0.1%
211.111
< 0.1%
2001
< 0.1%
192.861
< 0.1%
1901
< 0.1%
188.891
< 0.1%
183.331
< 0.1%

Interactions

2022-11-08T21:45:05.685376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:47.511152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:48.963152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:50.510154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:51.936154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:53.455154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:54.860152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:56.456150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:57.924844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:59.552859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:01.107860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:02.658860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:04.089373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:05.790371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:47.640150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:49.072152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:50.616154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:52.043154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:53.560155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:54.972152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:56.565843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:58.033846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:59.709861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:01.214858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:02.766862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:04.200373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:05.898376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:47.751150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:49.183152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:50.725154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:52.152155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:53.669155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:55.087152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:56.680847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:58.145846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:59.841861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:01.326860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:02.876860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:04.312372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:06.005375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:47.860152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:49.292152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:50.830150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:52.259155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:53.774154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:55.198150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:56.790845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:58.255847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:59.952860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:01.433860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:02.984860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:04.423373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:06.114376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:47.966149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:49.400149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:50.939153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:52.364155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:53.881153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:55.309150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:56.901844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:58.363843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:00.078860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:01.540860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:03.090858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:04.534373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:06.221373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:48.074149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:49.508152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:51.060154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:52.469149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:53.988153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:55.421150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:57.012846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:58.468841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:00.190858image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:01.648860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:03.199373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:04.646370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:06.333373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:48.186149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:49.623150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:51.175151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:52.582155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:54.100155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:55.539150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:57.131847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:44:58.584845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:00.306861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:01.762861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:03.311373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-08T21:45:04.764373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-11-08T21:45:05.448375image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-08T21:45:11.197374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-08T21:45:11.340373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-08T21:45:11.485370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-08T21:45:11.632373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-08T21:45:11.779373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-08T21:45:07.157371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-08T21:45:07.325373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

CODMunicípioCV_BCGCV_HEPatite_BCV_HIBCV_DPTCV_PolioCV_rotaCV_PneumoCV_MNCCCV_SCR1CV_SCR2CV_VARICELACV_HEPATITE_A
0110001110001 Alta Floresta D'Oeste67.5793.6993.6993.9991.2993.6997.0097.00115.1161.6388.2291.24
1110002110002 Ariquemes108.6983.2183.2883.2883.6885.0089.0588.3287.1759.4978.8880.82
2110003110003 Cabixi0.0092.7592.7592.7594.20101.45101.4589.8698.5194.0397.0197.01
3110004110004 Cacoal108.9888.4589.2889.2889.1390.0491.8592.75135.8427.3282.1982.04
4110005110005 Cerejeiras63.9492.1992.9492.9494.0595.5498.1495.91103.3783.9093.6392.88
5110006110006 Colorado do Oeste19.2189.6689.6689.6689.1693.6097.0493.10100.0084.7399.51102.46
6110007110007 Corumbiara14.0280.3780.3780.3786.9292.5295.3391.59106.86120.59124.51124.51
7110008110008 Costa Marques77.3795.2695.2695.7992.11102.63106.3299.47109.1983.7892.4392.97
8110009110009 Espigão D'Oeste91.3083.4483.4483.4484.0894.2797.6695.3392.292.7890.3686.94
9110010110010 Guajará-Mirim62.1247.3247.3247.3246.3946.2747.7946.5052.4415.9737.5438.50

Last rows

CODMunicípioCV_BCGCV_HEPatite_BCV_HIBCV_DPTCV_PolioCV_rotaCV_PneumoCV_MNCCCV_SCR1CV_SCR2CV_VARICELACV_HEPATITE_A
5560522160522160 Uruaçu93.5886.2386.2386.2380.9489.6293.7788.3093.7140.7665.3381.33
5561522170522170 Uruana22.9672.5972.5972.5971.8577.7886.6779.2676.8748.5163.4368.66
5562522180522180 Urutaí95.83170.83170.83170.83166.67150.00145.83175.00170.8320.83125.00129.17
5563522185522185 Valparaíso de Goiás20.6279.9479.9480.1080.7579.5882.0879.6677.8435.9051.0274.00
5564522190522190 Varjão37.93131.03131.03131.03131.03124.14127.59117.24111.11122.22203.70155.56
5565522200522200 Vianópolis125.00104.65104.65105.2399.42104.07111.63101.16105.8587.13119.88115.20
5566522205522205 Vicentinópolis9.3556.1256.1256.1257.5553.9654.6847.4872.4613.7734.0626.81
5567522220522220 Vila Boa64.71131.37131.37131.37129.41127.45131.37133.33123.5374.5182.35125.49
5568522230522230 Vila Propício28.3067.9267.9267.9267.9260.3867.9262.2656.6033.9643.4052.83
5569530010530010 Brasília104.8578.8278.7778.7678.7480.9084.4181.7587.1257.4678.9081.75